Experiment · architecture

MoE dense-compute smoke

MoE is a capacity feature until sparse gather/scatter or grouped matmul lands; it should not be marketed as a compute-saving win yet. Retained outcome: worked with caveat.

Outcome
worked with caveat
Family
architecture
Evidence confidence
exact
Record
infrastructure

Evidence readout

Dense MLP `842 K` params loss `6.09 -> 1.76` at `55.6 step/s`; MoE 4 experts top-2 `2.42 M` params loss `5.95 -> 1.68` at `29.3 step/s`

What the attempt taught

MoE is a capacity feature until sparse gather/scatter or grouped matmul lands; it should not be marketed as a compute-saving win yet.

Why it stopped or stayed bounded

The MoE architecture, save/load, sample, eval, and inspect paths work, but the current compute path runs every expert and therefore does not deliver sparse-dispatch savings.

Method and scope

  • Experiment family: architecture.
  • Record kind: infrastructure.

Disposition

Return to MoE only with MLX scatter_add/grouped matmul support or a scoped custom Metal sparse-dispatch kernel.

This retained disposition is historical evidence, not authorization to restart the experiment. A new run needs a fresh question, frozen evaluator, explicit resource budget, and scoped tracking issue.

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Source provenance

The normalized record comes from docs/attempts.json. The links below are the tracked evidence and explanatory sources preserved with the record.